Welcome to Covid19 Data Analysis and Visualization


Let's Import the modules

Task 2

Task 2.1: importing covid19 dataset

importing "Covid19_Confirmed_dataset.csv" from "./Dataset" folder.

Let's check the shape of the dataframe

Task 2.2: Delete the useless columns

Task 2.3: Aggregating the rows by the country

visualization always helps for better understanding of our data.

Task3: Calculating a good measure

we need to find a good measure reperestend as a number, describing the spread of the virus in a country.

task 3.1: caculating the first derivative of the curve

task 3.2: find maxmimum infection rate for China

Task 3.3: find maximum infection rate for all of the countries.

Task 3.4: create a new dataframe with only needed column

Task4:

Task 4.1 : importing the dataset

Task 4.2: let's drop the useless columns

Task 4.3: changing the indices of the dataframe

Task4.4: now let's join two dataset we have prepared

Corona Dataset :

wolrd happiness report Dataset :

Task 4.5: correlation matrix

The higher the correlation values, the higher the correlation between those two columns. We can see the factors that correspond to maximum infection rate.

Task 5: Visualization of the results

our Analysis is not finished unless we visualize the results in terms figures and graphs so that everyone can understand what you get out of our analysis

Task 5.1: Plotting GDP vs maximum Infection rate

Task 5.2: Plotting Social support vs maximum Infection rate

Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate

Task 5.4: Plotting Freedom to make life choices vs maximum Infection rate

Visualizing Daily Covid 19 Report using May 25 and November 8, 2020 Dataset

These visualizations are based on data as of May 25, and November 8, 2020. I have used the daily report data published by John Hopkins University for May 25, 2020. The next part of the code deals with loading the .csv data to our project.

Preprocessing the data

Now since our data has loaded successfully, the next step is to preprocess the data before using it for plotting. It will include :

The data can be grouped together by the ‘groupby’ function of the dataframe. It is similar to the GROUPBY statement in SQL.

Plotting the top 20 countries with the maximum number of confirmed cases

Plotting Confirmed and Active cases for the top 5 countries with the maximum number of confirmed cases

Plotting a Choropleth map on World Map

As of November 8, 2020

corona datase

datasets

Cases Over Time

Cases over the time

Graph after 1M cases

Bubble Plot

Epidemic Span

Weekly Statistics

Monthly statistics

credit:

Jaskeerat Singh Bhatia

Devakumar Kp